hyperDT

Fast hyperboloid decision tree algorithms

This repository contains code for the paper Fast Hyperboloid Decision Tree Algorithms (ICLR 2024), which you can find at one of these links:

• OpenReview
• ArXiv

Installation:

Local install

To install, run this from your repo directory:

git clone https://github.com/pchlenski/hyperdt
cd hyperdt
pip install -e .

If you are installing with e.g. a conda environment or virtualenv, you can find exact dependencies in requirements.txt. These are installable in the usual way:

pip install -r requirements.txt

Pip install

Additionally, hyperDT is available on PyPI. It can be pip installed as follows:

pip install hyperdt

Tutorial

A basic tutorial demonstrating key HyperDT functionality is available in notebooks/tutorial.ipynb.

Reproducibility and data availability

All figures and tables in the paper were generated using a combination of Python scripts and Jupyter notebooks. The notebooks used in development were filtered down to only those that remained relevant to the final paper and moved to the notebooks/archive directory. The notebooks directory contains a tutorial and symbolic links to notebooks of particular relevance to a figure, table, or section of a paper, named according to the section they reproduce.

hororf_benchmarks.py runs the benchmarks contributing to Tables 1, 5, and 6, and scaling_benchmarks.py runs the benchmarks contributing to Figures 6 and 7.

All relevant datasets, plus benchmarking code outputs, can be found on Google Drive.

Citation

To cite HyperDT, please use the following:

@inproceedings{
    chlenski2024fast,
    title={Fast Hyperboloid Decision Tree Algorithms},
    author={Philippe Chlenski and Ethan Turok and Antonio Khalil Moretti and Itsik Pe'er},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=TTonmgTT9X}
}